68 citations · 83 across the 16 of their papers we have counts for
6 papers · 1 filter
EEG-to-Gait Decoding via Phase-Aware Representation Learning
Xi Fu, Weibang Jiang, Rui Liu +2
Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent recognition and control. This stud…
Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformer
Muyun Jiang, Yi Ding, Wei Zhang +14
Covert speech involves imagining speaking without audible sound or any movements. Decoding covert speech from electroencephalogram (EEG) is challenging due to a limited understandi…
Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities
Wei-Bang Jiang, Xi Fu, Yi Ding +1
Multimodal physiological signals, such as EEG, ECG, EOG, and EMG, are crucial for healthcare and brain-computer interfaces. While existing methods rely on specialized architectures…
EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding
Xi Fu, Rui Liu, Aung Aung Phyo Wai +4
Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes, the need for accurate temporal and spectral feat…
Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers
Yi Ding, Joon Hei Lee, Shuailei Zhang +2
Learning the spatial topology of electroencephalogram (EEG) channels and their temporal dynamics is crucial for decoding attention states. This paper introduces EEG-PatchFormer, a…
EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces
Yi Ding, Yong Li, Hao Sun +5
Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs).…